A data engineer must update data quality rules across 1,000 AWS Glue Data Catalog tables due to changed business requirements. Which approach will accomplish this with the least operational overhead?
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Correct answer: Create an AWS Lambda function that makes an API call to AWS Glue Data Quality to make the edits..
Why this is the answer
The most efficient approach is to use an AWS Lambda function to make API calls to AWS Glue Data Quality. AWS Glue Data Quality rules are managed programmatically through its API, allowing for bulk updates. A Lambda function can iterate through the 1,000 tables and apply the necessary rule changes via UpdateDataQualityRuleset or similar API operations, minimizing manual effort and operational overhead. Creating an AWS Glue ETL pipeline for each table would be excessively complex and resource-intensive, as ETL jobs are for data transformation, not metadata management. An Amazon EMR cluster is also overkill and introduces significant overhead for simply updating metadata rules. Using the AWS Management Console is impractical for 1,000 tables due to the manual effort required.
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